A vehicle logo detecting system for vehicle is an important part of an intelligent transportation system. A vehicle logo is served as a unique mark of vehicle brand. A vehicle recognition system can be effectively assisted to match with the relative information of the vehicle if the vehicle logo is correctly recognized, which will be beneficial to determine the identity of the vehicle and improve a recognition rate of vehicle model. Vehicle recognition is widely applied in the field of intelligent transportation, such as vehicle model matching, vehicle information collection and false plate detection. Due to the difference in areas, shapes and textures of the vehicle logo in reality, varied background grids of the vehicle logo, and the difference in such features as the vehicle logo space position of the large vehicle and small vehicle, a traditional vehicle logo detecting method based on template matching is difficult to obtain a high detection success rate while consuming a large amount of time.
In recent years, vehicle logo detection problems have been extensively studied. The existing vehicle logo detection algorithms include: a vehicle logo detecting method based on Adaboost, a vehicle logo detecting method based on vehicle logo texture feature, a vehicle logo detecting method based on texture consistency, a vehicle logo detecting method based on vehicle logo background elimination, a vehicle logo detecting method based on template and a vehicle logo detecting method based on exhaustive search, and the like. According to the vehicle logo detecting method based on Adaboost, the Adaboost classifier is used to study the features of the vehicle logo and a sliding window is used to position and recognize the vehicle logo. This method can obtain a better vehicle logo detection result, but the time consumption is too long. The vehicle logo detecting method based on the texture feature of the vehicle feature comprises the steps of firstly coarsely positioning the vehicle logo using the license plate position, then accurately positioning the vehicle logo using the prior knowledge of the vehicle logo and the vehicle logo edge feature, and recognizing the vehicle logo positioning result using the training classifier of the support vector machine; while the method is difficult to accurately position the vehicle logo under the condition of the complicated vehicle logo background texture. The vehicle logo detecting method based on the texture consistency is used for distinguishing the vehicle logo texture feature from the vehicle logo background feature so as to accurately position the vehicle logo and recognize the vehicle logo. This method is applicable to the situation in which the vehicle logo is very different from the background texture feature. But in reality, the vehicle logo is difficult to distinguish from the background texture feature thereof. The vehicle logo detecting method based on vehicle logo background elimination comprises the steps of eliminating the vehicle logo background texture using a filtering algorithm so as to obtain the accurate vehicle logo positioning result, then describing the vehicle logo using SIFT and other feature descriptor and recognizing through the classifier. This method can greatly reduce the influence of the vehicle logo background on the vehicle logo positioning, but it is easy to eliminate the vehicle logo information and affects the vehicle logo positioning and recognizing result. The vehicle logo detecting method based on template matching comprises the step of positing the position and the recognition result of the vehicle logo by taking the vehicle logo as the template. This method is relatively affected by the vehicle logo background little, but the time consumption is too long. The vehicle logo detecting method based on exhaustive search comprises the steps of performing the exhaustive search on the target area using the sliding window, and judging the target for the vehicle logo using such HOG, SIFT and other descriptor to obtain the vehicle logo area and the vehicle logo type. This method is theoretically applicable to all models, but the sliding window is relatively slow, which greatly affects the practicability of the algorithm.
In conclusion, the current vehicle logo detection algorithms have the following shortcomings or deficiencies that:
1) the vehicle logo for small vehicle is mostly detected merely, so that it is unable to be applied to all models, and the applicability is not wide.
2) under the influence of illumination, inclination and complicated grid background of vehicle logo, the current algorithm is difficult to detect the vehicle logo accurately, and the robustness is weaker.
3) the consumption of time is long, the detection speed is slow, so that it is unable to meet the actual requirements of the high detection speed.